Revolutionary Algorithms
This work addresses dynamic optimization problems, which are common in fields like AI and operations research, but appears incremental as it builds on existing approaches.
The paper tackles the challenge of dynamic optimization by proposing revolutionary algorithms, which combine parallel genetic algorithms and cultural algorithms to improve efficiency in changing environments.
The optimization of dynamic problems is both widespread and difficult. When conducting dynamic optimization, a balance between reinitialization and computational expense has to be found. There are multiple approaches to this. In parallel genetic algorithms, multiple sub-populations concurrently try to optimize a potentially dynamic problem. But as the number of sub-population increases, their efficiency decreases. Cultural algorithms provide a framework that has the potential to make optimizations more efficient. But they adapt slowly to changing environments. We thus suggest a confluence of these approaches: revolutionary algorithms. These algorithms seek to extend the evolutionary and cultural aspects of the former to approaches with a notion of the political. By modeling how belief systems are changed by means of revolution, these algorithms provide a framework to model and optimize dynamic problems in an efficient fashion.